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Purpose: Craniofacial microsomia (CFM) is the result of a disturbance in embryologic development and is characterised by an asymmetric, mostly unilateral facial underdevelopment. The aim of this study is to understand the midfacial involvement in CFM using principal component analysis (PCA)., Materials and Methods: Pre-operative data from 19 CFM and 23 control patients were collected. A set of 71 landmarks was placed on three-dimensional (3D) reconstructions of all skulls to compare both populations. PCA visualised variation within both groups and calculated the vector of change. Linear measurements were taken to compare ratios between the populations and between the affected and unaffected sides in CFM patients., Results: PCA defined a vector that described shape changes between both populations. Videos showed the variation within the control and CFM group and the transformation from a mean CFM skull into a normal phenotype. Linear measurements showed a significant difference between the affected and unaffected sides in CFM patients., Conclusion: PCA has not been applied on asymmetrical data before, but it has proved to be a useful method to describe CFM. The virtual normalisation of a mean CFM skull enables visualisation of the bony shape changes, which is promising to delineate and to plan surgical correction and could be used as an outcome measure., (Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.)

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.